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- W4320802017 abstract "Water is the most important resource for sustaining life. Everyone has the right to have access to pollution-free water. The achievement of safe drinking water leads to tangible benefits to health. According to the World Health organization (WHO), the number of global deaths due to water diseases is about two and a half million deaths per year. Therefore, it is important to study new applications for analyzing and classifying safe water quality. In this research, the classification algorithms of K Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Artificial Neural Network (ANN), Logistic Regression (LR), and Support Vector Machine (SVM) are used to classify the potability of the drinking water. RF and DT achieved the highest performance with an accuracy of 83.78% and 74.98% respectively. The lowest classifier accuracy is LR with 48.74%. In addition, the most important features of the RF model are pH, followed by Hardness, followed by Sulfate. On other hand, the least important feature is Turbidity." @default.
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- W4320802017 date "2022-10-25" @default.
- W4320802017 modified "2023-10-16" @default.
- W4320802017 title "Classification of Water Potability Using Machine Learning Algorithms" @default.
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- W4320802017 doi "https://doi.org/10.1109/icdabi56818.2022.10041667" @default.
- W4320802017 hasPublicationYear "2022" @default.
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